Topology and seasonal evolution of the network of extreme precipitation over the Indian subcontinent and Sri Lanka

Abstract

Abstract. This paper employs a complex network approach to determine the topology and evolution of the network of extreme precipitation that governs the organization of extreme rainfall before, during, and after the Indian Summer Monsoon (ISM) season. We construct networks of extreme rainfall events during the ISM (June-September), post-monsoon (October-December), and pre-monsoon (March-May) periods from satellite-derived (Tropical Rainfall Measurement Mission, TRMM) and rain-gauge interpolated (Asian Precipitation Highly Resolved Observational Data Integration Towards the Evaluation of Water Resources, APHRODITE) data sets. The structure of the networks is determined by the level of synchronization of extreme rainfall events between different grid cells throughout the Indian subcontinent. Through the analysis of various complex-network metrics, we describe typical repetitive patterns in North Pakistan (NP), the Eastern Ghats (EG), and the Tibetan Plateau (TP). These patterns appear during the pre-monsoon season, evolve during the ISM, and disappear during the post-monsoon season. These are important meteorological features that need further attention and that may be useful in ISM timing and strength prediction.

Unified functional network and nonlinear time series analysis for complex systems science: Thepyunicornpackage

“…It opens new possibilities to account for North Pakistan as a key region for inferring interactions between the Indian Summer Monsoon system and Western Disturbances, and based on this information, to improve the forecasting of extreme rainfall events over the Indian subcontinent. 105 Analysis of the annual variability of the evolving Asian monsoon SAT climate network allows us to conclude that a highly non-random, general deterministic structure is present in the network on which the inter-annual variability is imprinted. 66,67,106 The annual climate network variability could be explained by a dominant influence of the topography of the region on the climate network as well as regular monsoon effects, or by dominant climatic events such as El Niño or La Niña.…”

“…66,102,103 In particular, evolving climate networks have been used to study seasonal and annual variability of the Indian Monsoon system as one of the major global climatic subsystems affecting life and prosperity of 1/4th of the world's human population. [104][105][106] On seasonal time scales, it is crucial to identify spatial structures of synchronicity of extreme rainfall events over the Indian monsoon domain, as extreme rainfall events are the main cause of devastating floods on the subcontinent. On annual time scales, variability of the surface air temperature (SAT) field is of great interest, as it influences the total amount of rainfall and its spatial distribution during the monsoon season.…”

Multi-scale event synchronization analysis for unravelling climate processes: a wavelet-based approach

“…Various methods for studying synchronization are available, based on recurrences (Marwan et al, 2007;Donner et al, 2010;Arnhold et al, 1999;Le Van Quyen et al, 1999;Quiroga et al, 2000Quiroga et al, , 2002Schiff et al, 1996), phase differences (Schiff et al, 1996;Rosenblum et al, 1997), or the quasi-simultaneous appearance of events (Tass et al, 1998;Stolbova et al, 2014;Malik et al, 2012;Rheinwalt et al, 2016). For the latter, the method of event synchronization (ES) has received popularity owing to its simplicity, in particular within the fields of brain (Pfurtscheller and Silva 1999;Krause et al, 1996) and cardiovascular research (O'Connor et al, 2013), non-linear chaotic systems (Callahan et al, 1990), and climate sciences (Tass et al, 1998;Stolbova et al, 2014;Malik et al, 2012;Rheinwalt et al, 2016)…”

“…For the latter, the method of event synchronization (ES) has received popularity owing to its simplicity, in particular within the fields of brain (Pfurtscheller and Silva 1999;Krause et al, 1996) and cardiovascular research (O'Connor et al, 2013), non-linear chaotic systems (Callahan et al, 1990), and climate sciences (Tass et al, 1998;Stolbova et al, 2014;Malik et al, 2012;Rheinwalt et al, 2016)…”

“…Particularly in climate sciences, ES has been successfully applied to capture driver-response relationships, time delays between spatially distributed processes, strength of synchronization, and moisture source and rainfall propagation trajectories, and to determine typical spatio-temporal patterns in monsoon systems (Stolbova et al, 2014;Malik et al, 2012;Rheinwalt et al, 2016). Furthermore, extensions of the ES approach have been suggested to increase its robustness with respect to boundary effects (Stolbova et al, 2014;Malik et al, 2012) and number of events (Rheinwalt et al, 2016).…”